By Per Nyberg, Chief Commercial Officer, Stradigi AI

AI Will No Longer Be for the Precious Few

AI in the enterprise has proven to be a slow-growth and experimental journey for companies that don't fall into the category of massive tech giants and Fortune 500 businesses. In the early stages, companies have toyed with machine learning-its capabilities and how they can apply it to their business. This stage is dependent on digital readiness, the skills in an organization and the business problem that needs to be solved. However, many companies have become overwhelmed and distracted with the constant buzz around the latest advancements in machine learning algorithms; leaving missed opportunities for right-sized, tangible implementations and progress.

In 2020, AI will be found in an expanded pool of business roles, use cases and companies of all sizes. AI is no longer limited to the precious few machine learning experts and data scientists. It's poised to enable business analysts, provide value for small- to medium-sized businesses (SMBs), and due to scarce talent and resources, it will require the right strategies for scaling.

AI for Many! The Rise of the AI-enabled Business Analyst

Businesses have been working to break through the logjam of AI projects that have been back-burnered in the face of machine learning skills shortages. However, we're seeing the real world reach of AI expand with more companies looking at ways to foster collaboration, gain economies of scale and accelerate their AI paths from concept to production with maturing tools. AI is no longer for the small minority of machine learning experts and data scientists. With data at their core, business analysts are also eager for a slice of the pie. With AI and ML tools at their disposal, the skills of business analysts are expanding towards data science to explore insights from more diverse and richer data sets through the use of machine learning. Technology and automated machine learning techniques will begin shifting the use of data and AI to a greater proportion of a company's business analysts. The demand for these skills are also starting to shape higher-ed curriculums to contend with this new wave of expectations.

AI Favors the Prepared, but the SMB Won't Wait

There are now clear AI use-cases in every industry and companies have been diligently progressing in their digital readiness, and today the question really is about which specific companies will execute on a clearly articulated AI strategy for their organization. Of course, we will see continued growth in the spaces hot for AI like retail, eCommerce, media and advertising, transportation, and logistics and manufacturing, however this growth will no longer only be driven by Fortune 500 enterprises and tech companies. I believe more SMBs will also make AI a priority. Like large enterprises, SMBs will be able to attain substantial benefits from AI to bring their businesses forward whether it's through automating repetitive tasks, generating insights from customer data and more. However, unlike their larger enterprises, they may be more agile and nimble to move quickly to leapfrog their competitors.

Scaling Scarce Specialist AI Resources and Gaining Broader Adoption Across the Enterprise

Speaking with our customers and prospects, one of the biggest concerns we hear about AI adoption is the shortage of machine learning skills making companies unable to see the light of day on their AI projects.  In addition many ML implementations continue to be focused on developing the pipelines and proving the applicability of ML with projects and models from scratch. This approach simply doesn't scale in many ways - from efficient reuse of learnings to accelerating the ideation cycle.  This is one area where an AI platform really makes sense. The key is for businesses to start scaling their specialists' skills and focus them on the most important tasks. Also, day-to-day AI adoption needs to extend beyond specialized data scientists. Companies can support data scientists, business analysts and other critical roles with intuitive AI platforms that can help take projects from the ideation phase and into production throughout the organization.

While we can expect the hype around AI to continue, practical, real-world implementations remain a priority for enterprises and will require careful strategy for scale. With more resources available to roles outside of the typical data scientist and machine learning engineer, and with more use cases covering the span of diverse industries and businesses of all sizes, 2020 will be the year AI moves from production to implementation and to insightful results.

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